Head Trauma

Automatic Segmentation and Radiomics for Identification and Activity Assessment of CTE Lesions in Crohn's Disease.

BACKGROUND: The purpose of this article is to develop a deep learning automatic segmentation model for the segmentation of Crohn's disease (CD) lesions in computed tomography enterography (CTE) images. Additionally, the radiomics features extracted from the segmented CD lesions will be analyzed and multiple machine learning classifiers will be built to distinguish CD activity.

Examining the Role of Relationship Alignment in Large Language Models

The rapid development and deployment of Generative AI in social settings raise important questions about how to optimally personalize them for users while maintaining accuracy and realism. Based on a Facebook public post-comment dataset, this study evaluates the ability of Llama 3.0 (70B) to predict the semantic tones across different combinations of a commenter's and poster's gender, age, and f...

Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative Models

Brain imaging has allowed neuroscientists to analyze brain morphology in genetic and neurodevelopmental disorders, such as Down syndrome, pinpointin...

CISCA and CytoDArk0: a Cell Instance Segmentation and Classification method for histo(patho)logical image Analyses and a new, open, Nissl-stained dataset for brain cytoarchitecture studies

Delineating and classifying individual cells in microscopy tissue images is inherently challenging yet remains essential for advancements in medical...

Deep Learning to Detect Intracranial Hemorrhage in a National Teleradiology Program and the Impact on Interpretation Time.

The diagnostic performance of an artificial intelligence (AI) clinical decision support solution for acute intracranial hemorrhage (ICH) detection was...

Role of Data-driven Regional Growth Model in Shaping Brain Folding Patterns

The surface morphology of the developing mammalian brain is crucial for understanding brain function and dysfunction. Computational modeling offers ...

zIA: a GenAI-powered local auntie assists tourists in Italy

The Tourism and Destination Management Organization (DMO) industry is rapidly evolving to adapt to new technologies and traveler expectations. Gener...

Standard compliant video coding using low complexity, switchable neural wrappers

The proliferation of high resolution videos posts great storage and bandwidth pressure on cloud video services, driving the development of next-gene...

Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.

AIMS: To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals,...

Personalised Speech-Based PTSD Prediction Using Weighted-Instance Learning.

Post-traumatic stress disorder (PTSD) is a prevalent disorder that can develop in people who have experienced very stressful, shocking, or distressing...

Acute Stress Disorder Detection using Machine Learning based on resting-state fMRI.

Early diagnosis of Acute Stress Disorder (ASD) is important, given its potential progression to post-traumatic system disorder (PTSD). The current dia...

Deep-Learning Approach for Tissue Classification using Acoustic Waves during Ablation with an Er:YAG Laser (Updated)

Today's mechanical tools for bone cutting (osteotomy) cause mechanical trauma that prolongs the healing process. Medical device manufacturers aim to...

[AI based Evaluation of Psychotrauma related to Lahars in the Commune of Prêcheur in the French Antilles].

Objectives Natural disasters have a significant impact on mental health. Data collected from the population offer a unique opportunity for post-disast...

Prediction of early-phase cytomegalovirus pneumonia in post-stem cell transplantation using a deep learning model.

BACKGROUND: Diagnostic challenges exist for CMV pneumonia in post-hematopoietic stem cell transplantation (post-HSCT) patients, despite early-phase ra...

Enhancing Post-Surgical Wound Care in Anorectal Diseases: A Comparative Study of Advanced Convolutional Neural Network (CNN) Architectures for Image Classification and Analysis.

AIM: Anorectal diseases, often requiring surgical intervention and careful post-operative wound management, pose substantial challenges in healthcare....

Drive Pressure-Guided Individualized Positive End-Expiratory Pressure in Traumatic Brain Injury Surgery: A Randomized Controlled Trial.

AIM: Intraoperative lung-protective ventilation strategies (LPVS) have been shown to improve lung oxygenation and prevent postoperative pulmonary prob...

Real-Time Monitoring for Aseptic Drilling: Lessons Learned from the Atacama Rover Astrobiology Drilling Studies Contamination Control Strategy and Implementation and Application to the Icebreaker Mars Life Detection Mission.

In 2019, the Atacama Rover Astrobiology Drilling Studies (ARADS) project field-tested an autonomous rover-mounted robotic drill prototype for a 6-Sol ...

A Tailorable Robotic Hand Orthosis to Support Children with Neurological Hand Impairments: a Case Study in a Child's Home.

Neurological disorders such as traumatic brain injuries (TBI) can lead to hand impairments in children, negatively impacting their quality of life. Fu...

2D Wavelet-Scalogram Deep-Learning for Seizures Pattern Identification in the Post-Hypoxic-Ischemic EEG of Preterm Fetal Sheep.

Neonatal seizures after an hypoxic-ischemic (HI) event in preterm newborns can contribute to neural injury and cause impaired brain development. Prete...

Artificial intelligence, machine learning, and deep learning in liver transplantation.

Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predom...